This　paper　proposes　a　novel　approach　called　Stack-GA(2)M　to　identify　fraudulent　reviewers　in　an　inherently　interpretable　manner　by　fusing　both　target　and　non-target　features.　Specifically,　for　local　interpretability,　we　adopt　GA(2)M　(Standard　Generalized　Additive　Model　plus　interactions)　as　the　basic　classifier　to　produce　three　subordinate　models　trained　by　using　the　target　features　and　the　non-target　features　as　review　textual　features　and　reviewer　behavioral　features.　For　global　interpretability,　we　adopt　LR　(Logistic　Regression)　as　the　meta　classifier　to　stack　the　outputs　of　three　subordinate　models　to　identify　the　fraudulent　reviewers.　The　white-box　model　of　LR　enables　us　to　understand　the　global　interpretability　of　the　target　features　and　the　non-target　features　in　identifying　fraudulent　reviewers.　With　GA(2)M,　the　local　interpretability　of　each　subordinate　model　is　derived　by　using　feature　importance,　spline　shape　functions　for　individual　features,　and　heatmaps　for　interaction　terms.　Extensive　experiments　on　Yelp　dataset　demonstrate　that　the　proposed　Stack-GA(2)M　approach　is　superior　to　state-of-the-art　techniques　in　identifying　fraudulent　reviewers　and　exhibits　favorable　inherent　interpretability.